# import ldm.modules.encoders.modules
# import open_clip
# import torch
# import transformers.utils.hub
#
# from modules import shared
#
#
# class ReplaceHelper:
#     def __init__(self):
#         self.replaced = []
#
#     def replace(self, obj, field, func):
#         original = getattr(obj, field, None)
#         if original is None:
#             return None
#
#         self.replaced.append((obj, field, original))
#         setattr(obj, field, func)
#
#         return original
#
#     def restore(self):
#         for obj, field, original in self.replaced:
#             setattr(obj, field, original)
#
#         self.replaced.clear()
#
#
# class DisableInitialization(ReplaceHelper):
#     """
#     When an object of this class enters a `with` block, it starts:
#     - preventing torch's layer initialization functions from working
#     - changes CLIP and OpenCLIP to not download model weights
#     - changes CLIP to not make requests to check if there is a new version of a file you already have
#
#     When it leaves the block, it reverts everything to how it was before.
#
#     Use it like this:
#     ```
#     with DisableInitialization():
#         do_things()
#     ```
#     """
#
#     def __init__(self, disable_clip=True):
#         super().__init__()
#         self.disable_clip = disable_clip
#
#     def replace(self, obj, field, func):
#         original = getattr(obj, field, None)
#         if original is None:
#             return None
#
#         self.replaced.append((obj, field, original))
#         setattr(obj, field, func)
#
#         return original
#
#     def __enter__(self):
#         def do_nothing(*args, **kwargs):
#             pass
#
#         def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs):
#             return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
#
#         def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
#             res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
#             res.name_or_path = pretrained_model_name_or_path
#             return res
#
#         def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
#             args = args[0:3] + ('/', ) + args[4:]  # resolved_archive_file; must set it to something to prevent what seems to be a bug
#             return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
#
#         def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
#
#             # this file is always 404, prevent making request
#             if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
#                 return None
#
#             try:
#                 res = original(url, *args, local_files_only=True, **kwargs)
#                 if res is None:
#                     res = original(url, *args, local_files_only=False, **kwargs)
#                 return res
#             except Exception:
#                 return original(url, *args, local_files_only=False, **kwargs)
#
#         def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
#             return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
#
#         def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):
#             return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
#
#         def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
#             return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
#
#         self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
#         self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
#         self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
#
#         if self.disable_clip:
#             self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
#             self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
#             self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
#             self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
#             self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
#             self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
#
#     def __exit__(self, exc_type, exc_val, exc_tb):
#         self.restore()
#
#
# class InitializeOnMeta(ReplaceHelper):
#     """
#     Context manager that causes all parameters for linear/conv2d/mha layers to be allocated on meta device,
#     which results in those parameters having no values and taking no memory. model.to() will be broken and
#     will need to be repaired by using LoadStateDictOnMeta below when loading params from state dict.
#
#     Usage:
#     ```
#     with sd_disable_initialization.InitializeOnMeta():
#         sd_model = instantiate_from_config(sd_config.model)
#     ```
#     """
#
#     def __enter__(self):
#         if shared.cmd_opts.disable_model_loading_ram_optimization:
#             return
#
#         def set_device(x):
#             x["device"] = "meta"
#             return x
#
#         linear_init = self.replace(torch.nn.Linear, '__init__', lambda *args, **kwargs: linear_init(*args, **set_device(kwargs)))
#         conv2d_init = self.replace(torch.nn.Conv2d, '__init__', lambda *args, **kwargs: conv2d_init(*args, **set_device(kwargs)))
#         mha_init = self.replace(torch.nn.MultiheadAttention, '__init__', lambda *args, **kwargs: mha_init(*args, **set_device(kwargs)))
#         self.replace(torch.nn.Module, 'to', lambda *args, **kwargs: None)
#
#     def __exit__(self, exc_type, exc_val, exc_tb):
#         self.restore()
#
#
# class LoadStateDictOnMeta(ReplaceHelper):
#     """
#     Context manager that allows to read parameters from state_dict into a model that has some of its parameters in the meta device.
#     As those parameters are read from state_dict, they will be deleted from it, so by the end state_dict will be mostly empty, to save memory.
#     Meant to be used together with InitializeOnMeta above.
#
#     Usage:
#     ```
#     with sd_disable_initialization.LoadStateDictOnMeta(state_dict):
#         model.load_state_dict(state_dict, strict=False)
#     ```
#     """
#
#     def __init__(self, state_dict, device, weight_dtype_conversion=None):
#         super().__init__()
#         self.state_dict = state_dict
#         self.device = device
#         self.weight_dtype_conversion = weight_dtype_conversion or {}
#         self.default_dtype = self.weight_dtype_conversion.get('')
#
#     def get_weight_dtype(self, key):
#         key_first_term, _ = key.split('.', 1)
#         return self.weight_dtype_conversion.get(key_first_term, self.default_dtype)
#
#     def __enter__(self):
#         if shared.cmd_opts.disable_model_loading_ram_optimization:
#             return
#
#         sd = self.state_dict
#         device = self.device
#
#         def load_from_state_dict(original, module, state_dict, prefix, *args, **kwargs):
#             used_param_keys = []
#
#             for name, param in module._parameters.items():
#                 if param is None:
#                     continue
#
#                 key = prefix + name
#                 sd_param = sd.pop(key, None)
#                 if sd_param is not None:
#                     state_dict[key] = sd_param.to(dtype=self.get_weight_dtype(key))
#                     used_param_keys.append(key)
#
#                 if param.is_meta:
#                     dtype = sd_param.dtype if sd_param is not None else param.dtype
#                     module._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device, dtype=dtype), requires_grad=param.requires_grad)
#
#             for name in module._buffers:
#                 key = prefix + name
#
#                 sd_param = sd.pop(key, None)
#                 if sd_param is not None:
#                     state_dict[key] = sd_param
#                     used_param_keys.append(key)
#
#             original(module, state_dict, prefix, *args, **kwargs)
#
#             for key in used_param_keys:
#                 state_dict.pop(key, None)
#
#         def load_state_dict(original, module, state_dict, strict=True):
#             """torch makes a lot of copies of the dictionary with weights, so just deleting entries from state_dict does not help
#             because the same values are stored in multiple copies of the dict. The trick used here is to give torch a dict with
#             all weights on meta device, i.e. deleted, and then it doesn't matter how many copies torch makes.
#
#             In _load_from_state_dict, the correct weight will be obtained from a single dict with the right weights (sd).
#
#             The dangerous thing about this is if _load_from_state_dict is not called, (if some exotic module overloads
#             the function and does not call the original) the state dict will just fail to load because weights
#             would be on the meta device.
#             """
#
#             if state_dict is sd:
#                 state_dict = {k: v.to(device="meta", dtype=v.dtype) for k, v in state_dict.items()}
#
#             original(module, state_dict, strict=strict)
#
#         module_load_state_dict = self.replace(torch.nn.Module, 'load_state_dict', lambda *args, **kwargs: load_state_dict(module_load_state_dict, *args, **kwargs))
#         module_load_from_state_dict = self.replace(torch.nn.Module, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(module_load_from_state_dict, *args, **kwargs))
#         linear_load_from_state_dict = self.replace(torch.nn.Linear, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(linear_load_from_state_dict, *args, **kwargs))
#         conv2d_load_from_state_dict = self.replace(torch.nn.Conv2d, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(conv2d_load_from_state_dict, *args, **kwargs))
#         mha_load_from_state_dict = self.replace(torch.nn.MultiheadAttention, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(mha_load_from_state_dict, *args, **kwargs))
#         layer_norm_load_from_state_dict = self.replace(torch.nn.LayerNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(layer_norm_load_from_state_dict, *args, **kwargs))
#         group_norm_load_from_state_dict = self.replace(torch.nn.GroupNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(group_norm_load_from_state_dict, *args, **kwargs))
#
#     def __exit__(self, exc_type, exc_val, exc_tb):
#         self.restore()
